Personalized learning can be done anywhere, but to do it well, change is required in every tier of a school system.

That’s the view of Richard Culatta, who helped lead Rhode Island’s Personalized Learning Initiative in 2016 with a mission of spreading this model of learning across the state and developing best practices in laboratory schools.

Part of the challenge is that the phrase, which is growing in popularity, is very difficult to define. Some districts are quick to equate personalized learning with technology alone — a mentality that has caused many to fail, said Culatta, who now leads the International Society for Technology in Education, a global nonprofit organization that shares best practices in education technology among its nearly 16,000 members.

“You can’t buy personalized learning,” Culatta said — but “I’ve never seen a successful implementation of personalized learning without some tech to support it.”

Giving Compass' Take:
• In this post at Philanthropy 2173, author Lucy Bernholz examines the role of technology in the social sector and how its effect on civil society needs to be considered more carefully before adoption.
• How can we protect the world's most vulnerable people from unintended consequences of tech (such as AI that inherently discriminates against a certain group)? What can nonprofit leaders do to stay ahead of the game?
• Here's why the digital age needs better ethical and legal guidelines.
Most digital technologies are designed by, or at least brought to mass adoption by, commercial enterprises. This is often, but not always done, on the back of government funded infrastructure or research. Civil society, which exists as a counterbalance to and with some intended separation from, both markets and governments, often adopts new technologies without first considering how the tools might conflict with the sector’s own values.
Some technological approaches, such as artificial intelligence (AI), have attracted enough public detractors that industry is responding with its own policy association “principles of practice,” bringing an undeniable stamp of “regulation pre-emption.” AI, of course, has been in the public’s mind for decades, courtesy of robot cartoons and science fiction. There's a robust debate among tech leaders about the future of AI.
For other technologies, where the need for placating the public is less urgent, the typical deployment strategy goes something like this:
R&D, either in universities or industry
experimentation in specific applications,
commercial take up where experimentation is successful
rampant application to broader business opportunities,
crossover experimentation on social issues
belated social sector response when application generates “unintended” (though not necessarily unpredictable) consequences
Is there a better way to do this?

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